Socioeconomic status, life expectancy and mortality in a universal healthcare setting: An individual-level analysis of >6 million Catalan residents

2019 ◽  
Vol 123 ◽  
pp. 91-94 ◽  
Author(s):  
Usama Bilal ◽  
Miguel Cainzos-Achirica ◽  
Montse Cleries ◽  
Sebastià Santaeugènia ◽  
Xavier Corbella ◽  
...  
Keyword(s):  
Socioeconomic Status ◽  
Life Expectancy ◽  
Healthcare Setting ◽  
Individual Level ◽  
Universal Healthcare ◽  
Level Analysis

scholarly journals WAITING TIME AND SOCIOECONOMIC STATUS-AN INDIVIDUAL-LEVEL ANALYSIS

2013 ◽  
Vol 23 (4) ◽  
pp. 446-461 ◽  
Author(s):  
Karin Monstad ◽  
Lars Birger Engesaeter ◽  
Birgitte Espehaug
Keyword(s):  
Socioeconomic Status ◽  
Waiting Time ◽  
Individual Level ◽  
Level Analysis

scholarly journals An Association of Total Health Expenditure with GDP and Life Expectancy

2017 ◽  
Vol 1 (2) ◽  
pp. AU7-AU12 ◽  
Author(s):  
Sojib Bin Zaman ◽  
Naznin Hossain ◽  
Varshil Mehta ◽  
Shuchita Sharmin ◽  
Shakeel Ahmed Ibne Mahmood
Keyword(s):  
Developing Countries ◽  
Life Expectancy ◽  
Gross Domestic Product ◽  
Health Expenditure ◽  
Health Spending ◽  
Domestic Product ◽  
Fiscal Year ◽  
Total Health Expenditure ◽  
Registration System ◽  
Individual Level

Introduction: Gradual  total health expenditure (THE) has become a major concern. It is not only the increased THE, but also its unequal growth in  overall economy, found among the developing countries. If increased life expectancy is considered as a leverage for an individual’s investment in health services, it can be  expected that as the life expectancy increases, tendency of health care investment will also experience a boost up. Objective: The aim of the present study was to explore and identify the association of healthcare expenditure with the life expectancy and Gross Domestic Product (GDP) in developing countries, especially that of Bangladesh. Methodology: Data were retrospectively collected from “Health Bulletin 2011” and “Sample Vital Registration System 2010” of Bangladesh considering the fiscal year 1996 to fiscal year 2006. Using STATA, multivariable logistic regression was performed to find out the association of total health expenditure with GDP and life expectancy. Results: A direct relationship between GDP and total health expenditure was found through analysing the data. At the individual level, income  had a direct influence on health spending. However, there was no significant relationship between total health expenditure with increased life expectancy. Conclusion: The present study did not find any association between life expectancy and total health expenditure. However, our analysis found out that total health expenditure is more sensitive to gross domestic product rather than life expectancy.

scholarly journals Neighborhood Disadvantage and Active, Disabled and Total Life Expectancy Among Community-living Older Persons

2021 ◽  
Author(s):  
Thomas M. Gill ◽  
Emma X. Zang ◽  
Terrence E. Murphy ◽  
Linda Leo-Summers ◽  
Evelyne A. Gahbauer ◽  
...  
Keyword(s):  
Prognostic Factors ◽  
Life Expectancy ◽  
Older Persons ◽  
Neighborhood Disadvantage ◽  
Community Living ◽  
Remaining Life ◽  
Disadvantaged Group ◽  
Individual Level ◽  
Total Life Expectancy ◽  
Total Life

AbstractBackgroundNeighborhood disadvantage is a novel social determinant of health that could adversely affect the functional well-being and longevity of older persons. We evaluated whether estimates of active, disabled and total life expectancy differ on the basis of neighborhood disadvantage after accounting for individual-level socioeconomic characteristics and other prognostic factors.MethodsWe used data on 754 community-living older persons from South Central Connecticut, who completed monthly assessments of disability from 1998 to 2020. Scores on the area deprivation index were dichotomized at the 80th state percentile to distinguish neighborhoods that were disadvantaged (81-100) from those that were not (1-80).ResultsWithin 5-year age increments from 70 to 90, active and total life expectancy were consistently lower in participants from neighborhoods that were disadvantaged versus not disadvantaged, and these differences persisted and remained statistically significant after adjustment for individual-level race/ethnicity, education, income, and other prognostic factors. At age 70, adjusted estimates (95% CI) for active and total life expectancy (in years) were 12.3 (11.5-13.1) and 15.0 (13.8-16.1) in the disadvantaged group and 14.2 (13.5-14.7) and 16.7 (15.9-17.5) in the non-disadvantaged group. At each age, participants from disadvantaged neighborhoods spent a greater percentage of their projected remaining life disabled, relative to those from non-disadvantaged neighborhoods, with adjusted values (SE) ranging from 17.7 (0.8) vs. 15.3 (0.5) at age 70 to 55.0 (1.7) vs. 48.1 (1.3) at age 90.ConclusionsLiving in a disadvantaged neighborhood is associated with lower active and total life expectancy and a greater percentage of projected remaining life disabled.

scholarly journals Individual-level analysis of differential expression of genes and pathways for personalized medicine

2014 ◽  
Vol 31 (1) ◽  
pp. 62-68 ◽  
Author(s):  
Hongwei Wang ◽  
Qiang Sun ◽  
Wenyuan Zhao ◽  
Lishuang Qi ◽  
Yunyan Gu ◽  
...  
Keyword(s):  
Personalized Medicine ◽  
Differential Expression ◽  
Individual Level ◽  
Expression Of Genes ◽  
Differential Expression Of Genes ◽  
Level Analysis

Appendix for The COVID-19 Pandemic and Life Expectancy: Decomposition Using Individual-Level Mortality Data

2021 ◽  
Author(s):  
Paula Natalia Barreto Parra ◽  
Vladimir A. Atanasov ◽  
John Meurer ◽  
Jeffrey Whittle ◽  
Qian Luo ◽  
...  
Keyword(s):  
Life Expectancy ◽  
Mortality Data ◽  
Individual Level

Levels of Aggregation in Conjoint Analysis: An Empirical Comparison

1980 ◽  
Vol 17 (4) ◽  
pp. 516-523 ◽  
Author(s):  
William L. Moore
Keyword(s):  
Conjoint Analysis ◽  
Predictive Power ◽  
Aggregate Level ◽  
Segmentation Method ◽  
Empirical Comparison ◽  
Individual Level ◽  
Aggregate Analysis ◽  
The Individual ◽  
Level Analysis

Two segmented methods of performing conjoint anal/sis, clustered and componential segmentation, are compared with each other as well as with individual level and totally aggregate level analyses. The two segmented methods provide insights to the data that (1) are not obtainable at the aggregate level and (2) are in a form that is more easily communicated than the information from the individual level analysis. The predictive power of the clustered segmentation method is higher than that of componential segmentation, and both are superior to the aggregate analysis but inferior to individual level analysis.

scholarly journals Does neighborhood socioeconomic status predict the risk of preterm birth? A community-based Canadian cohort study

BMJ Open ◽  
2019 ◽  
Vol 9 (2) ◽  
pp. e025341 ◽  
Author(s):  
Kamala Adhikari ◽  
Scott B Patten ◽  
Tyler Williamson ◽  
Alka B Patel ◽  
Shahirose Premji ◽  
...  
Keyword(s):  
Socioeconomic Status ◽  
Cohort Study ◽  
Preterm Birth ◽  
Cohort Studies ◽  
False Positive ◽  
False Positive Rate ◽  
Community Based ◽  
Individual Level ◽  
Risk Threshold ◽  
Positive Rate

ObjectiveThis study developed and internally validated a predictive model for preterm birth (PTB) to examine the ability of neighbourhood socioeconomic status (SES) to predict PTB.DesignCohort study using individual-level data from two community-based prospective pregnancy cohort studies (All Our Families (AOF) and Alberta Pregnancy Outcomes and Nutrition (APrON)) and neighbourhood SES data from the 2011 Canadian census.SettingCalgary, Alberta, Canada.ParticipantsPregnant women who were <24 weeks of gestation and >15 years old were enrolled in the cohort studies between 2008 and 2012. Overall, 5297 women participated in at least one of these cohorts: 3341 women participated in the AOF study, 2187 women participated in the APrON study and 231 women participated in both studies. Women who participated in both studies were only counted once.Primary and secondary outcome measuresPTB (delivery prior to 37 weeks of gestation).ResultsThe rates of PTB in the least and most deprived neighbourhoods were 7.54% and 10.64%, respectively. Neighbourhood variation in PTB was 0.20, with an intra-class correlation of 5.72%. Neighbourhood SES, combined with individual-level predictors, predicted PTB with an area under the receiver-operating characteristic curve (AUC) of 0.75. The sensitivity was 91.80% at a low-risk threshold, with a high false-positive rate (71.50%), and the sensitivity was 5.70% at a highest risk threshold, with a low false-positive rate (0.90%). An agreement between the predicted and observed PTB demonstrated modest model calibration. Individual-level predictors alone predicted PTB with an AUC of 0.60.ConclusionAlthough neighbourhood SES combined with individual-level predictors improved the overall prediction of PTB compared with individual-level predictors alone, the detection rate was insufficient for application in clinical or public health practice. A prediction model with better predictive ability is required to effectively find women at high risk of preterm delivery.

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